{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# seaborn.catplot(）分类图\n",
    "\n",
    "解析：\n",
    "\n",
    "  1. catplot() 分类图(它是下面8种图的接口，下面八种图表均可通过指定kind参数来绘制)\n",
    "  2. stripplot() 分类散点图\n",
    "  3. swarmplot() 能够显示分布密度的分类散点图\n",
    "  4. boxplot() 箱图\n",
    "  5. violinplot() 小提琴图\n",
    "  6. boxenplot() 增强箱图\n",
    "  7. pointplot() 点图\n",
    "  8. barplot() 条形图\n",
    "  9. countplot() 计数图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "seaborn.catplot(x=None, y=None, hue=None,\n",
    "                data=None, row=None, col=None,\n",
    "                col_wrap=None, estimator=<function mean>, ci=95, \n",
    "                n_boot=1000, units=None, order=None,\n",
    "                hue_order=None, row_order=None, col_order=None, \n",
    "                kind='strip', height=5, aspect=1, orient=None, \n",
    "                color=None, palette=None, legend=True,\n",
    "                legend_out=True, sharex=True, sharey=True, \n",
    "                margin_titles=False, facet_kws=None, **kwargs)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参数解读： 必须的参数data 其他参数均为可选；\n",
    "\n",
    "data:是DataFrame类型的;\n",
    "\n",
    "\n",
    "x,y为数据中变量的名称;\n",
    "\n",
    "row，col：数据中变量的名称\n",
    "\n",
    "作用：设置分类变量将决定网格的分面。\n",
    "\n",
    "kind：字符串\n",
    "\n",
    "要绘制的绘图类型(对应于分类绘图功能的名称:\"count\"-统计图, \"point\"-点, \"bar\"-条形, \"strip\"-条形, \"swarm\"-群形, \"box\"-框形, \"violin\"-小提琴形, or\"boxen\"-盒形.）\n",
    "\n",
    "col_wrap:int类型数值\n",
    "\n",
    "作用：让每行显示指定数量的图，如果超过该数量，则多行显示。\n",
    "\n",
    "orient:方向：v或者h\n",
    "\n",
    "作用：设置图的绘制方向(垂直或水平),如何选择：一般是根据输入变量的数据类型(dtype)推断出来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>diet</th>\n",
       "      <th>pulse</th>\n",
       "      <th>time</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>85</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>low fat</td>\n",
       "      <td>88</td>\n",
       "      <td>30 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>90</td>\n",
       "      <td>1 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>low fat</td>\n",
       "      <td>92</td>\n",
       "      <td>15 min</td>\n",
       "      <td>rest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  id     diet  pulse    time  kind\n",
       "0           0   1  low fat     85   1 min  rest\n",
       "1           1   1  low fat     85  15 min  rest\n",
       "2           2   1  low fat     88  30 min  rest\n",
       "3           3   2  low fat     90   1 min  rest\n",
       "4           4   2  low fat     92  15 min  rest"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "exercise = sns.load_dataset(\"exercise\")\n",
    "exercise[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.475x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\")\n",
    "# 获取数据\n",
    "exercise = sns.load_dataset(\"exercise\")\n",
    "\"\"\"\n",
    "案例1：基本分类图\n",
    "\"\"\"\n",
    "sns.catplot(x=\"time\", y=\"pulse\", hue=\"kind\", data=exercise)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.475x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\")\n",
    "# 获取数据\n",
    "exercise = sns.load_dataset(\"exercise\")\n",
    "\"\"\"\n",
    "案例2： 通过设置kind来指定绘制的图类型\n",
    "\n",
    "kind=\"violin\" 则表示绘制小提琴图\n",
    "\"\"\"\n",
    "sns.catplot(x=\"time\", y=\"pulse\", hue=\"kind\",data=exercise, kind=\"violin\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 796.475x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\")\n",
    "# 获取数据\n",
    "exercise = sns.load_dataset(\"exercise\")\n",
    "\"\"\"\n",
    "案例3：根据col分类，以列布局绘制多列图\n",
    "设置col，根据指定的col的变量名，以列的形式显示(eg.col='diet',则在列的方向上显示，显示图的数量为diet列中对值去重后的数量)\n",
    "\"\"\"\n",
    "sns.catplot(x=\"time\", y=\"pulse\", hue=\"kind\",col=\"diet\", data=exercise)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 537.275x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\")\n",
    "# 获取数据\n",
    "exercise = sns.load_dataset(\"exercise\")\n",
    "\"\"\"\n",
    "案例4：绘图时，设置图(facets)的高度和宽度比\n",
    "\"\"\"\n",
    "sns.catplot(x=\"time\", y=\"pulse\", hue=\"kind\",col=\"diet\", data=exercise,height=4, aspect=.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "5         0       3    male   NaN      0      0   8.4583        Q   Third   \n",
       "6         0       1    male  54.0      0      0  51.8625        S   First   \n",
       "7         0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "8         1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "9         1       2  female  14.0      1      0  30.0708        C  Second   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  \n",
       "5    man        True  NaN   Queenstown    no   True  \n",
       "6    man        True    E  Southampton    no   True  \n",
       "7  child       False  NaN  Southampton    no  False  \n",
       "8  woman       False  NaN  Southampton   yes  False  \n",
       "9  child       False  NaN    Cherbourg   yes  False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "# 使用 titanic数据集\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>G</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>D</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>D</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived  pclass     sex   age  sibsp  parch      fare embarked   class  \\\n",
       "1          1       1  female  38.0      1      0   71.2833        C   First   \n",
       "3          1       1  female  35.0      1      0   53.1000        S   First   \n",
       "6          0       1    male  54.0      0      0   51.8625        S   First   \n",
       "10         1       3  female   4.0      1      1   16.7000        S   Third   \n",
       "11         1       1  female  58.0      0      0   26.5500        S   First   \n",
       "21         1       2    male  34.0      0      0   13.0000        S  Second   \n",
       "23         1       1    male  28.0      0      0   35.5000        S   First   \n",
       "27         0       1    male  19.0      3      2  263.0000        S   First   \n",
       "31         1       1  female   NaN      1      0  146.5208        C   First   \n",
       "52         1       1  female  49.0      1      0   76.7292        C   First   \n",
       "\n",
       "      who  adult_male deck  embark_town alive  alone  \n",
       "1   woman       False    C    Cherbourg   yes  False  \n",
       "3   woman       False    C  Southampton   yes  False  \n",
       "6     man        True    E  Southampton    no   True  \n",
       "10  child       False    G  Southampton   yes  False  \n",
       "11  woman       False    C  Southampton   yes   True  \n",
       "21    man        True    D  Southampton   yes   True  \n",
       "23    man        True    A  Southampton   yes   True  \n",
       "27    man        True    C  Southampton    no  False  \n",
       "31  woman       False    B    Cherbourg   yes  False  \n",
       "52  woman       False    D    Cherbourg   yes  False  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "# 获取数据\n",
    "#去掉deck这一列中值为空的数据\n",
    "data=titanic[titanic.deck.notnull()]\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\")\n",
    "# 获取数据\n",
    "#去掉deck这一列中值为空的数据\n",
    "data=titanic[titanic.deck.notnull()]\n",
    "\"\"\"\n",
    "案例5：绘制柱状图 kind=\"count\"\n",
    "设置col_wrap一个数值，让图每行只显示数量为该数值得列，多余的另起一行显示\n",
    "\"\"\"\n",
    "sns.catplot(x=\"alive\", col=\"deck\", col_wrap=4,data=data,kind=\"count\", height=2.5, aspect=.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504.85x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"ticks\")\n",
    "# 获取数据\n",
    "#去掉deck这一列中值为空的数据\n",
    "data=titanic[titanic.deck.notnull()]\n",
    "# 水平绘图，并将其他关键字参数传递给绘图函数\n",
    "\"\"\"\n",
    "案例6：利用catplot()绘制小提琴图 kind=\"violin\"\n",
    "\n",
    "orient设置图的方向\n",
    "\"\"\"\n",
    "sns.catplot(x=\"age\", y=\"embark_town\",hue=\"sex\", row=\"class\",\n",
    "            data=data,\n",
    "            orient=\"h\", height=2, aspect=3, palette=\"Set3\",\n",
    "            kind=\"violin\",dodge=True,  cut=0, bw=.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整理制作：数据挖掘分析可视化学研社：\n",
    "\n",
    "<div>\n",
    "<table align=\"left\" border=\"1\" bordercolor=\"#000000\">\n",
    "    <div>\n",
    "    <tr>\n",
    "        <td>\n",
    "            微信公众号：数据挖掘分析可视化学研社<br>\n",
    "            <img src=\"../image/visual.jpg\" width=\"150\" height=\"150\" align=\"left\"/>    \n",
    "        </td>\n",
    "    </tr>\n",
    "    </div>\n",
    "    <div>\n",
    "     <tr>\n",
    "        <td>\n",
    "        配置环境：python 3.4+  \n",
    "        </td>\n",
    "    </tr>\n",
    "        </div>\n",
    "</table>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
